Kit and system for auxiliary diagnosis of children autism

Abstract

The invention discloses a kit and a system for auxiliary diagnosis of children autism, and relates to the technical field of medical diagnosis. The kit and the system disclosed by the invention take arachidonic acid, tetracosanoic acid, S-adenosylmethionine, oxidized glutathione, glutathione, S-adenosylhomocysteine, choline, glycine, L-serine, cystathionine, L-cysteine, taurine, folic acid or vitamin B12 as biomarkers, and are used for auxiliary diagnosis of children autism; the specificity and the sensitivity are relatively high.

Description

Technical Field

[0001] The present invention relates to the technical field of medical diagnosis, and in particular to a kit and system for auxiliary diagnosis of childhood autism. Background Art

[0002] Autism syndrome (ASD) is a heterogeneous neurodevelopmental disorder with early onset and chronic characteristics. ASD is defined as a psychiatric disorder whose primary diagnostic criteria are impaired social interaction and communication, and restricted and repetitive behaviors and interests. Therefore, factors influencing neurodevelopment are associated with the underlying etiology of ASD, which may lead to adverse neurobehavioral, cognitive, and mental health outcomes in individuals. The etiology of ASD is not fully understood, and multiple complex etiologies may coexist, involving genetics, the environment, and gene-environment interactions.

[0003] The diagnosis of ASD usually relies on scales for assessment, and currently, there is a lack of biomarkers with high diagnostic performance.

[0004] In view of this, the present invention is proposed. Summary of the Invention

[0005] The present invention aims to provide a kit and system for auxiliary diagnosis of autism syndrome (ASD) in children. The kit and system provided by the present invention can detect several biomarkers in a targeted manner and can be used for auxiliary diagnosis of ASD in children with high specificity and sensitivity.

[0006] On the one hand, the present invention provides a kit for auxiliary diagnosis of autism in children, which includes a reagent for detecting the concentration of a biomarker, wherein the biomarker is selected from a combination of one or more of the following components: arachidonic acid, tetracosanoic acid, S-adenosylmethionine, oxidized glutathione, glutathione, S-adenosylhomocysteine, choline, glycine, L-serine, cystathionine, L-cysteine, taurine, folic acid and vitamin B12.

[0007] By testing and screening a large number of samples, the present invention discovered for the first time that the above-mentioned biomarkers showed significant differences between the ASD group and the healthy group. Further ROC analysis revealed that these biomarkers have high diagnostic performance and can be used for early screening or auxiliary diagnosis of ASD, improving the diagnostic accuracy of the disease and providing a diagnostic basis for early intervention treatment.

[0008] Alternatively, in some embodiments of the present invention, the biomarker is selected from arachidonic acid, lignoceric acid, or a combination thereof.

[0009] The present invention demonstrates for the first time that ASD patients have significantly abnormal fatty acid levels (arachidonic acid and lignoceric acid). When arachidonic acid is used as a diagnostic marker for ASD, its AUC reaches 0.6; when lignoceric acid is used as a diagnostic marker for ASD, its AUC reaches 0.62. Both assays have high diagnostic accuracy, with a sensitivity of 71.43% and a specificity of 54.26%. This invention provides a novel molecule that can serve as a diagnostic marker for ASD, offering a reliable molecular biomarker for the auxiliary diagnosis of ASD.

[0010] Optionally, in some embodiments of the present invention, the biomarker is selected from taurine.

[0011] The present invention first discovered that the fatty acid taurine level in ASD patients is significantly abnormal. When taurine is used as a diagnostic marker for ASD, its AUC can reach 0.6729; it has high diagnostic performance, with a sensitivity of 75.57% and a specificity of 48.74%.

[0012] Alternatively, in some embodiments of the present invention, the biomarker is selected from S-adenosylmethionine, S-adenosylhomocysteine, choline, L-cysteine, taurine, or any combination thereof.

[0013] The embodiments of the present invention discovered for the first time that when S-adenosylmethionine, S-adenosylhomocysteine, choline, L-cysteine, and taurine are combined as diagnostic markers for ASD, the AUC can reach 0.9691; it has extremely high diagnostic performance, with a sensitivity of 91.43% and a specificity of 85.71%.

[0014] Optionally, in some embodiments of the present invention, the kit further comprises a quality control reagent; the quality control reagent comprises arachidonic acid, tetracosanoic acid, S-adenosylmethionine, oxidized glutathione, glutathione, S-adenosylhomocysteine, choline, glycine, L-serine, cystathionine, L-cysteine, taurine, folic acid or vitamin B12.

[0015] On the other hand, the present invention provides a reagent for detecting biomarkers for use in preparing a kit for auxiliary diagnosis of autism in children, wherein the biomarker is selected from a combination of one or more of the following components: arachidonic acid, tetracosanoic acid, S-adenosylmethionine, oxidized glutathione, glutathione, S-adenosylhomocysteine, choline, glycine, L-serine, cystathionine, L-cysteine, taurine, folic acid and vitamin B12.

[0016] Alternatively, in some embodiments of the present invention, the biomarker is selected from arachidonic acid, lignoceric acid, or a combination thereof.

[0017] Optionally, in some embodiments of the present invention, the biomarker is selected from taurine.

[0018] Alternatively, in some embodiments of the present invention, the biomarker is selected from S-adenosylmethionine, S-adenosylhomocysteine, choline, L-cysteine, taurine, or any combination thereof.

[0019] It should be noted that the concentration levels of the biomarkers disclosed in the present invention can be detected using conventional methods in the art, such as high performance liquid chromatography-mass spectrometry, enzyme-linked immunosorbent assay (ELISA), etc.

[0020] On the other hand, the present invention provides a system for auxiliary diagnosis of autism in children, which includes an information acquisition module, which is used to obtain concentration information of biomarkers of a patient to be diagnosed, wherein the biomarkers are selected from a combination of one or more of the following components: arachidonic acid, tetracosanoic acid, S-adenosylmethionine, oxidized glutathione, glutathione, S-adenosylhomocysteine, choline, glycine, L-serine, cystathionine, L-cysteine, taurine, folic acid and vitamin B12.

[0021] Alternatively, in some embodiments of the present invention, the biomarker is selected from arachidonic acid, lignoceric acid, or a combination thereof.

[0022] Optionally, in some embodiments of the present invention, the biomarker is selected from taurine.

[0023] Alternatively, in some embodiments of the present invention, the biomarker is a combination of S-adenosylmethionine, S-adenosylhomocysteine, choline, L-cysteine ​​and taurine.

[0024] Optionally, in some embodiments of the present invention, the detection sample of the above biomarkers is from plasma.

[0025] Optionally, in some embodiments of the present invention, the system further comprises a diagnostic module configured to determine the ASD status of the patient to be diagnosed based on the concentration information of the biomarker. Optionally, in some embodiments of the present invention, when arachidonic acid is used as a marker, if the concentration of arachidonic acid is less than or equal to 509.30 μmol / L, it indicates that the patient to be diagnosed is at risk of having an ASD.

[0026] Alternatively, in some embodiments of the present invention, when tetracosanoic acid is used as a marker, if the concentration of arachidonic acid is lower than or equal to 38.46 μmol / L, it indicates that the patient to be diagnosed is at risk of having ASD.

[0027] Alternatively, in some embodiments of the present invention, when taurine is used as a marker, if the concentration of taurine is equal to or greater than 38.46 μmol / L, it indicates that the patient to be diagnosed has a high risk of suffering from ASD. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0029] Figure 1 2 is the ROC curve for diagnosing ASD using arachidonic acid or lignoceric acid as markers in Example 1.

[0030] Figure 2 This is the ROC curve for diagnosing ASD using taurine as a marker in Example 2.

[0031] Figure 3 2 is the ROC curve of the diagnosis of ASD using the combination of S-adenosylmethionine, S-adenosylhomocysteine, choline, L-cysteine, and taurine as markers in Example 2. DETAILED DESCRIPTION

[0032] To make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely below. Where specific conditions are not specified in the embodiments, conventional conditions or conditions recommended by the manufacturer are used. Where the manufacturer of the reagents or instruments is not specified, all are conventional products that can be purchased commercially.

[0033] The features and performance of the present invention are further described in detail below with reference to the embodiments.

[0034] Example 1

[0035] In this example, arachidonic acid or lignoceric acid is used as a marker to verify its diagnostic performance as a marker for ASD.

[0036] 1. Sample source

[0037] From December 2023 to December 2024, a total of 70 children diagnosed with autism and 61 typically developing controls at Guizhou Provincial People's Hospital participated in this study. Eligible children with ASD met the following criteria: 1) a confirmed diagnosis of ASD according to the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, by a qualified and experienced psychiatrist; 2) age between 2 and 6 years at the time of recruitment; and 3) willingness of parents or legal guardians to participate and provide plasma samples from children with ASD. Exclusion criteria included 1) symptomatic autism (autism-related disorders such as Rett syndrome and Fragile X syndrome); 2) psychiatric illness; 3) neurological disease; 4) metabolic disease; and 5) required supplements before and during sample collection. In addition, a total of 61 typically developing children undergoing preschool entrance physical examinations were recruited from the physical examination clinic of Guizhou Provincial People's Hospital. Inclusion criteria for the typically developing group were age matching with the ASD group but without any psychiatric, neurological, or metabolic diseases.

[0038] 2 Ethical approval

[0039] The experiment in this example was conducted in accordance with the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Guizhou Provincial People's Hospital (No. 2023-0462023.6.2).

[0040] 3 Clinical data collection

[0041] After enrollment, demographic information was collected using a self-administered questionnaire, which included variables such as age, sex, height, weight, parental education level, and parental occupation. In addition, data on medication use, adherence to special diets (including therapeutic diets such as ketogenic, gluten-free, or low-protein diets, and dietary patterns influenced by family or religious beliefs, such as vegetarian or pork-free diets), gastrointestinal (GI) symptoms, and nutritional supplement intake were collected through clinical interviews.

[0042] Participants diagnosed with ASD were assessed for autism symptoms using the Autism Spectrum Rating Scale (ASRS) total score. The ASRS is an instrument with established reliability and validity in China. Regarding gastrointestinal symptoms, participants were assessed for recent (within the past three months) picky eating, constipation, vomiting, abdominal pain, and diarrhea. The presence of any of these symptoms was categorized as a gastrointestinal symptom.

[0043] 4. Biological sample collection

[0044] In the morning, 3 mL of blood samples were collected from children who had fasted overnight (12 hours) using EDTA as an anticoagulant. Plasma was immediately separated by centrifugation at 3000 g for 10 minutes at 4°C. The obtained plasma samples were aliquoted into five Eppendorf tubes (1.5 mL), 200 μL each, and frozen at −80°C until analysis. Metabolomic analysis was performed within 3 months of storage.

[0045] 5UPLC-MS / MS Methodology

[0046] The GC-FID / MS system used in this embodiment is composed of an Agilent 7890B gas chromatograph and an Agilent 5977B mass spectrometer, and the mass spectrometer includes a flame ionization detector (Agilent Technologies, USA). A 10m long Agilent DB-225 capillary gas chromatograph column with an inner diameter of 0.1mm and a film thickness of 0.1μm was used, with an injection volume of 2μL and a split ratio of 1:20. Helium was used as the carrier gas and nitrogen as the supplementary gas. The injection port and detector temperature were maintained at 230°C. The column temperature program was started at 55°C for 1 minute and then increased to 205°C at a speed of 30°C / min. The temperature was then maintained at 205°C for 3 minutes, then increased to 230°C at a speed of 5°C / min, and maintained at this temperature for another 1 minute. Mass spectra were obtained using electron ionization (EI) at a voltage of 70eV over a mass-to-charge ratio (m / z) range of 45-450.

[0047] All analyses were performed using Mass Hunter software. Retention times were qualitatively compared with standard compounds and verified by mass spectrometry. Identification was performed using an internal standard. Mass Hunter Workstation software (Agilent, version B.08.00) facilitated peak identification and integration of peak areas. Characterization of methylated fatty acids involved reference to chromatograms generated from 37 established standard mixtures, followed by verification using their corresponding mass spectral data. Concentrations of individual fatty acids were determined based on FID data relative to the internal standard.

[0048] 6 Statistical analysis

[0049] Descriptive statistics were performed overall and by group. The characteristics of the study participants, including all fatty acids, are shown as means (SD) for normally distributed variables, medians (IQR) for skewed variables, and frequencies and percentages for categorical variables. Comparisons between groups were performed using the 2-sample t-test, Kruskal-Wallis test, and chi-square test, respectively. To reduce confounding bias, a multivariate linear regression model was performed on the estimated coefficients, with 95% confidence intervals (CIs) adjusted for age (y), sex, BMI z-score, parental education level (graduate, bachelor, undergraduate), parental occupation (civil servant, technical worker, freelancer), and gastrointestinal symptoms (yes, no). To further confirm the biomarkers of ASD, receiver operating characteristic (ROC) analysis was performed.

[0050] 7 Results

[0051] 7.1 Characteristics of Participants

[0052] A total of 70 children with ASD and 61 typically developing (TD) children participated in this study. The mean age of the ASD group was 3.48 ± 1.43 years, while that of the TD group was 3.67 ± 0.97 years. The sociodemographic characteristics of the two groups were generally similar, except that the majority of children diagnosed with childhood ASD were boys (87.14%) compared to the control group (54.09%). Patients with ASD had a higher prevalence of gastrointestinal symptoms, with an ASRS score of 122.31 ± 19.24. Details are shown in Table 1.

[0053] Table 1 Comparison of general information between the ASD group and the control group

[0054]

[0055] Note: Measurement data follow normal distribution. Indicates that the comparison between groups was performed with the independent sample t test; if the group did not obey the normal distribution, [M(P 25 ,P 75 )], and Mann-Whitney U was used for comparison between groups; count data were expressed as frequency (n) and percentage (%), and x was used for comparison between groups. 2 test; ﹡ There was statistically significant difference in gender distribution between the two groups (P<0.05); BMI—body mass index.

[0056] 7.2 Differences in fatty acid profiles among groups

[0057] Compared with the TD group, the ASD group had lower levels of most saturated fatty acids (SFA) and unsaturated fatty acids. Notably, compared with the control group, the ASD group had significantly lower C24:0 (34.70±7.92 μmol / L vs. 38.34±8.73 μmol / L, t=5.473, P=0.019), and the ASD group had significantly lower C20:4n6 concentrations (457.36±105.26 μmol / L vs. 493.31±111.86 μmol / L, t=4.047, P=0.044) (Table 2).

[0058] Table 2 Fatty acid concentrations between ASD and TD groups

[0059]

[0060]

[0061] Abbreviations: TD, typical development; ASD, autism spectrum disorder; Autism Spectrum Rating Scale.

[0062] 1. Kruskal-Wallis group equality rank test was performed for non-normally distributed variables, and chi-square test was performed for categorical variables.

[0063] 2. Values ​​are means (±SD).

[0064] 7.3 ROC Analysis

[0065] In ROC analysis (see Figure 1 ), compared with the TD group, the AUC (area under ROC) values ​​of the two fatty acids were higher than 0.5 (P < 0.05, Figure 1 ).

[0066] The optimal cutoff point for using C20:4n6 (arachidonic acid, AA) as an autism biomarker was 509.30 μmol / L. When the AA concentration of a patient was lower than or equal to this concentration, it indicated a high risk of autism. This cutoff point had a sensitivity of 71.43% and a specificity of 52.46% (AUC = 0.60, 95% confidence interval (CI), 0.50-0.70, P = 0.044).

[0067] The optimal cutoff point for using C24:0 (tetracosanoic acid, LA) as an autism biomarker was 38.46 μmol / L. When the LA concentration in a patient is lower than or equal to this concentration, it indicates a high risk of autism. This cutoff point had a sensitivity of 71.43% and a specificity of 52.46% (AUC = 0.62, 95% CI, 0.52-0.72, P = 0.019).

[0068] The above results indicate that arachidonic acid and tetracosanoic acid can be used as auxiliary diagnostic markers for ASD with good specificity and sensitivity.

[0069] Example 2

[0070] This example validates S-adenosylmethionine, S-adenosylhomocysteine, choline, L-cysteine, and taurine as diagnostic markers for ASD.

[0071] 1. Sample source

[0072] From June to October 2023, a total of 59 children diagnosed with ASD and 40 neurodevelopmentally normal controls were recruited from Guizhou Provincial People's Hospital. Eligible children with ASD met the following criteria: 1) a diagnosis of ASD by a qualified and experienced psychiatrist according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria; 2) age between 2 and 5 years at the time of recruitment; and 3) parental or legal guardian willingness to participate and provide a plasma sample from the child with ASD. Exclusion criteria included: 1) symptomatic autism (such as autism-related disorders such as Rett syndrome and Fragile X syndrome); 2) psychiatric illness; 3) neurological disease; 4) metabolic disease; and 5) the use of necessary supplements before and after sample collection. In addition, 40 typically developing children undergoing preschool entrance physical examinations were recruited from the Physical Examination Center of Guizhou Provincial People's Hospital. Inclusion criteria for the typically developing group matched the autism group for age, but without psychological, neurological, or metabolic diseases.

[0073] 2UPLC-MS / MS method

[0074] Targeted metabolomics was used to analyze metabolite levels, which were divided into four different groups according to different detection methods and conditions: nine amino acids [glycine, L-serine, cystathionine, taurine, L-methionine, L-cysteine, DL-homocysteine, oxidized glutathione (GSSG), and reduced glutathione (GSH)]; four choline metabolites (choline, betaine, dimethylglycine, and sarcosine); four water-soluble vitamins [4-pyridoxalic acid (VB6-PA), pyridoxal pyrophosphate (VB6-PLP), 5-methyltetrahydrofolate (5-MeTHF), and methylmalonic acid (MMA)]; and five folate metabolites [total homocysteine, S-adenosylmethionine (SAM), S-adenosylhomocysteine ​​(SAH), folic acid, and tetrahydrofolate]. Analyses were performed using an Agilent 1290 UPLC coupled to an Agilent 6470 Triple Quadrupole Mass Spectrometer equipped with an electrospray ionization (ESI) source (Agilent Technologies). Liquid chromatography analysis of amino acid metabolites was performed using an Agilent ZORBAX Eclipse Plus C18 column (2.1 × 100 mm, 1.8 μm particles), while choline metabolites were analyzed using a Waters ACQUITY UPLC BEH Amide column (2.1 × 100 mm, 1.7 μm particles). Liquid chromatography analysis of four water-soluble vitamin metabolites was performed using a Waters ACQUITY UPLC HSS T3 column (2.1 × 100 mm, 1.8 μm particles), while folate metabolites were analyzed using an Agilent ZORBAX Eclipse Plus C18 column (2.1 × 100 mm, 1.8 μm particles). Mass spectrometry analysis of all metabolites was performed in positive ion mode using ESI.

[0075] 3ROC analysis

[0076] The results showed that 10 of the 10 factors were significantly different in autistic children compared with neurologically normal controls. Specifically, the levels of S-adenosylmethionine (SAM), oxidized glutathione (GSSG), and glutathione (GSH) were increased, while the levels of S-adenosylhomocysteine ​​(SAH), choline, glycine, L-serine, cystathionine, L-cysteine, and taurine were significantly decreased.

[0077] Among them, when using taurine as an autism biomarker, the optimal cutoff point is 62.03 μmol / L. When the arachidonic acid concentration of the patient to be diagnosed is lower than or equal to this concentration, it indicates that the patient is at high risk of autism. The sensitivity of this cutoff point is 78.57% and the specificity is 48.74% (AUC = 0.6729, 95% confidence interval (95% CI: 0.5761-0.7697, P = 0.0014) (see Figure 2 ).

[0078] When a combination of S-adenosylmethionine, S-adenosylhomocysteine, choline, L-cysteine, and taurine was used as a biomarker for autism (the number of samples was from 119), the optimal cutoff point obtained by the logistic regression model was 0.2252. A value higher than or equal to this cutoff value indicated that the person being diagnosed had a high risk of autism; the sensitivity of this cutoff point was 91.43% and the specificity was 85.71% (AUC = 0.9691, 95% CI: 0.94433-0.99387, P = 0.0126) (ROC curve see Figure 3 ).

[0079] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A kit for auxiliary diagnosis of autism in children, characterized in that: The method comprises a reagent for detecting the concentration of a biomarker, wherein the biomarker is selected from a combination of one or more of the following components: arachidonic acid, tetracosanoic acid, S-adenosylmethionine, oxidized glutathione, glutathione, S-adenosylhomocysteine, choline, glycine, L-serine, cystathionine, L-cysteine, taurine, folic acid and vitamin B12.

2. The kit according to claim 1, wherein The biomarker is selected from arachidonic acid, lignoceric acid, or a combination thereof.

3. The kit according to claim 1, wherein The biomarker is taurine.

4. The kit according to claim 1, wherein The biomarkers are a combination of S-adenosylmethionine, S-adenosylhomocysteine, choline, L-cysteine ​​and taurine.

5. The kit according to any one of claims 1 to 4, characterized in that The kit further includes a quality control reagent; the quality control reagent includes arachidonic acid, tetracosanoic acid, S-adenosylmethionine, oxidized glutathione, glutathione, S-adenosylhomocysteine, choline, glycine, L-serine, cystathionine, L-cysteine, taurine, folic acid or vitamin B12.

6. Use of a reagent for detecting biomarkers in the preparation of a kit for auxiliary diagnosis of autism in children, characterized in that: The biomarker is selected from one or more combinations of the following components: arachidonic acid, tetracosanoic acid, S-adenosylmethionine, oxidized glutathione, glutathione, S-adenosylhomocysteine, choline, glycine, L-serine, cystathionine, L-cysteine, taurine, folic acid and vitamin B12.

7. The use according to claim 6, characterized in that The biomarker is selected from arachidonic acid, lignoceric acid, or a combination thereof.

8. The use according to claim 6, characterized in that The biomarker is taurine.

9. The use according to claim 6, characterized in that The biomarker is a combination of S-adenosylmethionine, S-adenosylhomocysteine, choline, L-cysteine, and taurine.

10. A system for auxiliary diagnosis of autism in children, characterized in that: It includes an information acquisition module, which is used to obtain the concentration information of biomarkers of a patient to be diagnosed, wherein the biomarkers are selected from a combination of one or more of the following components: arachidonic acid, tetracosanoic acid, S-adenosylmethionine, oxidized glutathione, glutathione, S-adenosylhomocysteine, choline, glycine, L-serine, cystathionine, L-cysteine, taurine, folic acid and vitamin B12.

Images